Predictive Claims Platform for Vehicle Repair Cost Estimation
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Solution Overview
Problem
Insurance claim processing is often time-consuming, expensive, and complex, with inefficiencies in estimating repair costs leading to lengthy negotiations between insurance providers, repair facilities, and customers.
Innovation Solution
A predictive claims computing platform that determines the type of insurance coverage needed for vehicle repairs, identifies a suitable repair facility location, assigns a standard reimbursement amount, transmits this amount to the facility, receives actual repair costs, and adjusts the reimbursement based on differences, thereby streamlining the claims process and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed estimates are provided by highly trained individuals, then measurement precision of repair costs is improved, but loss of time and increase in cost occur
Solution Approach 1:
The patent replaces the manual estimation process performed by highly trained individuals with an automated machine learning system. The system uses algorithms to predict repair costs based on damage assessment data, eliminating the need for manual calculations while maintaining accuracy. This substitution of mechanical (manual) work with automated computational processes directly reduces time loss while preserving measurement precision.
Solution Approach 2:
The system transforms the estimation process from qualitative manual assessment to quantitative automated calculation. By changing the parameters of the estimation process—using structured data inputs (damage type, vehicle model, repair category) and algorithmic processing instead of human expert judgment—the system achieves both speed and accuracy. The parameter transformation enables rapid processing while maintaining precision through standardized calculation frameworks.
2Adaptability or versatility
If manual estimation processes are used, then adaptability to specific repair scenarios is improved, but device complexity and process complexity increase
Solution Approach 1:
The system segments the complex claims processing into distinct functional modules: damage assessment module, cost prediction module, negotiation module, and settlement module. Each module handles a specific aspect of the process independently, making the overall system more manageable and easier to implement. This segmentation reduces device complexity by breaking down the estimation process into standardized, reusable components that can be applied across different scenarios.
Solution Approach 2:
The patent introduces an intermediary automated system between the insurance provider and repair facility that standardizes the estimation process. This intermediary layer translates complex repair scenarios into standardized data inputs for the machine learning model, then provides structured output recommendations. The intermediary simplifies communication and coordination between parties while maintaining adaptability to various repair scenarios through the model's training data.
3Reliability
If traditional estimation and negotiation processes are used, then reliability of final repair cost determination is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary automated estimation and cost determination before the actual repair work begins. By pre-calculating repair costs using machine learning algorithms and providing standardized estimates upfront, the system eliminates the need for post-repair negotiations. This preliminary action establishes reliable cost determinations in advance, reducing the overall cycle time while maintaining reliability through algorithmic consistency and data-driven accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where actual repair costs are compared against predicted costs, and the model is continuously refined based on this feedback. This feedback loop ensures that the reliability of cost determination improves over time as the system learns from actual outcomes. The feedback mechanism also allows for real-time adjustments and refinements, maintaining high accuracy while processing claims more efficiently.
4Manufacturing precision
If highly trained claims adjusters perform estimation, then manufacturing precision of repair cost calculation is improved, but use of energy and operational cost increase
Solution Approach 1:
The patent replaces the human resource-intensive manual estimation process with an automated machine learning system. Instead of employing highly trained claims adjusters for each estimation task, the system uses computational algorithms that process damage data and generate cost predictions automatically. This substitution dramatically reduces operational costs and resource consumption while maintaining or improving calculation precision through algorithmic consistency and access to comprehensive training data.
Solution Approach 2:
The system enables self-service automated estimation where the machine learning model independently performs cost calculations without requiring human intervention. The system self-processes damage assessment data, applies learned patterns and relationships, and generates cost predictions autonomously. This self-service capability eliminates the need for expensive human expertise in each individual case, reducing operational costs while maintaining precision through the model's trained accuracy.
Data Source
AI summary
A system for a predictive claims computing platform may comprise a plurality of vehicles, a computing device associated with a repair facility, a network, and a server. The server may be configured to determine an insurance coverage type needed for repairing each vehicle in the plurality of vehicles, identify a location of the repair facility for repairing each vehicle, assign a standard amount to reimburse the repair facility for repairing each vehicle based on insurance coverage type and the location of the repair facility, transmit the standard amount to the computing device associated with the repair facility, receive from the computing device, a cost for actual repair of each vehicle after the actual repair for each vehicle has been completed, and adjust the standard amount to reimburse the repair facility for future repairs based on identifying a difference between the standard amount and the actual repair cost for each vehicle.